OpenRaiser/Intern-Atlas huggingface dataset:
Cell-Edge-Aware Precoding for Downlink Massive MIMO Cellular Networks
Venue: arXiv cs.IT 2016
Year: 2016

We propose a cell-edge-aware (CEA) zero forcing (ZF) precoder that exploits the excess spatial degrees of freedom provided by a large number of base station (BS) antennas to suppress inter-cell interference at the most vulnerable user equipments (UEs). We evaluate the downlink performance of CEA-ZF, as well as that of a conventional cell-edge-unaware (CEU) ZF precoder in a network with random base station topology. Our analysis and simulations show that the proposed CEA-ZF precoder outperforms CEU-ZF precoding in terms of (i) aggregate per-cell data rate, (ii) coverage probability, and (iii) 95%-likely, or edge user, rate. In particular, when both perfect channel state information and a large number of antennas N are available at the BSs, we demonstrate that the outage probability under CEA-ZF and CEU-ZF decay as 1/N^2 and 1/N, respectively. This result identifies CEA-ZF as a more effective precoding scheme for massive MIMO cellular networks. Our framework also reveals the importance of scheduling the optimal number of UEs per BS, and confirms the necessity to control the amount of pilot contamination received during the channel estimation phase.
A Maturity Model for Public Administration as Open Translation Data Providers
Venue: arXiv cs.CY 2016
Year: 2016

Any public administration that produces translation data can be a provider of useful reusable data to meet its own translation needs and the ones of other public organizations and private companies that work with texts of the same domain. These data can also be crucial to produce domain-tuned Machine Translation systems. The organization's management of the translation process, the characteristics of the archives of the generated resources and of the infrastructure available to support them determine the efficiency and the effectiveness with which the materials produced can be converted into reusable data. However, it is of utmost importance that the organizations themselves first become aware of the goods they are producing and, second, adapt their internal processes to become optimal providers. In this article, we propose a Maturity Model to help these organizations to achieve it by identifying the different stages of the management of translation data that determine the path to the aforementioned goal.
Estimating the Dissemination of Social and Mobile Search in Categories of Information Needs Using Websites as Proxies
Venue: arXiv cs.CY 2016
Year: 2016

With the increasing popularity of social means to satisfy information needs using Social Media (e.g., Social Media Question Asking, SMQA) or Social Information Retrieval approaches, this paper tries to identify types of information needs which are inherently social and therefore better suited for those techniques. We describe an experiment where prominent websites from various content categories are used to represent their respective content area and allow to correlate attributes of the content areas. The underlying assumption is that successful websites for focused content areas perfectly align with the information seekers' requirements when satisfying information needs in the respective content areas. Based on a manually collected dataset of URLs from websites covering a broad range of topics taken from Alexa (http://www.alexa.com} (retrieved 2015-11-04)) (a company that publishes statistics about web traffic), a crowdsourcing approach is employed to rate the information needs that could get solved by the respective URLs according to several dimensions (incl. sociality and mobility) to investigate possible correlations with other attributes. Our results suggest that information needs which do not require a certain formal expertise play an important role in social information retrieval and that some content areas are better suited for social information retrieval (e.g., Factual Knowledge & News, Games, Lifestyle) than others (e.g., Health & Lifestyle).
DeepChrome: Deep-learning for predicting gene expression from histone modifications
Venue: arXiv cs.LG 2016
Year: 2016

Motivation: Histone modifications are among the most important factors that control gene regulation. Computational methods that predict gene expression from histone modification signals are highly desirable for understanding their combinatorial effects in gene regulation. This knowledge can help in developing 'epigenetic drugs' for diseases like cancer. Previous studies for quantifying the relationship between histone modifications and gene expression levels either failed to capture combinatorial effects or relied on multiple methods that separate predictions and combinatorial analysis. This paper develops a unified discriminative framework using a deep convolutional neural network to classify gene expression using histone modification data as input. Our system, called DeepChrome, allows automatic extraction of complex interactions among important features. To simultaneously visualize the combinatorial interactions among histone modifications, we propose a novel optimization-based technique that generates feature pattern maps from the learnt deep model. This provides an intuitive description of underlying epigenetic mechanisms that regulate genes. Results: We show that DeepChrome outperforms state-of-the-art models like Support Vector Machines and Random Forests for gene expression classification task on 56 different cell-types from REMC database. The output of our visualization technique not only validates the previous observations but also allows novel insights about combinatorial interactions among histone modification marks, some of which have recently been observed by experimental studies.
Hybrid Information Flow Analysis for Programs with Arrays
Venue: arXiv cs.PL 2016
Year: 2016

Information flow analysis checks whether certain pieces of (confidential) data may affect the results of computations in unwanted ways and thus leak information. Dynamic information flow analysis adds instrumentation code to the target software to track flows at run time and raise alarms if a flow policy is violated; hybrid analyses combine this with preliminary static analysis. Using a subset of C as the target language, we extend previous work on hybrid information flow analysis that handled pointers to scalars. Our extended formulation handles arrays, pointers to array elements, and pointer arithmetic. Information flow through arrays of pointers is tracked precisely while arrays of non-pointer types are summarized efficiently. A prototype of our approach is implemented using the Frama-C program analysis and transformation framework. Work on a full machine-checked proof of the correctness of our approach using Isabelle/HOL is well underway; we present the existing parts and sketch the rest of the correctness argument.
Turchin's Relation for Call-by-Name Computations: A Formal Approach
Venue: arXiv cs.PL 2016
Year: 2016

Supercompilation is a program transformation technique that was first described by V. F. Turchin in the 1970s. In supercompilation, Turchin's relation as a similarity relation on call-stack configurations is used both for call-by-value and call-by-name semantics to terminate unfolding of the program being transformed. In this paper, we give a formal grammar model of call-by-name stack behaviour. We classify the model in terms of the Chomsky hierarchy and then formally prove that Turchin's relation can terminate all computations generated by the model.
Siamese Regression Networks with Efficient mid-level Feature Extraction for 3D Object Pose Estimation
Venue: arXiv cs.CV 2016
Year: 2016

In this paper we tackle the problem of estimating the 3D pose of object instances, using convolutional neural networks. State of the art methods usually solve the challenging problem of regression in angle space indirectly, focusing on learning discriminative features that are later fed into a separate architecture for 3D pose estimation. In contrast, we propose an end-to-end learning framework for directly regressing object poses by exploiting Siamese Networks. For a given image pair, we enforce a similarity measure between the representation of the sample images in the feature and pose space respectively, that is shown to boost regression performance. Furthermore, we argue that our pose-guided feature learning using our Siamese Regression Network generates more discriminative features that outperform the state of the art. Last, our feature learning formulation provides the ability of learning features that can perform under severe occlusions, demonstrating high performance on our novel hand-object dataset.
Diclique clustering in a directed random graph
Venue: arXiv cs.SI 2016
Year: 2016

We discuss a notion of clustering for directed graphs, which describes how likely two followers of a node are to follow a common target. The associated network motifs, called dicliques or bi-fans, have been found to be key structural components in various real-world networks. We introduce a two-mode statistical network model consisting of actors and auxiliary attributes, where an actor i decides to follow an actor j whenever i demands an attribute supplied by j. We show that the digraph admits nontrivial clustering properties of the aforementioned type, as well as power-law indegree and outdegree distributions.
Strengthening Hardness Results to 3-Connected Planar Graphs
Venue: arXiv cs.CC 2016
Year: 2016

In this paper we extend some classical NP-hardness results from the class of 2-connected planar graphs to subclasses of 3-connected planar graphs. The reduction are partly based on a new graph augmentation, which may be of independent interest.
On the Complexity of Realizing Facial Cycles
Venue: arXiv cs.CG 2016
Year: 2016

We study the following combinatorial problem. Given a planar graph $G=(V,E)$ and a set of simple cycles $\mathcal C$ in $G$, find a planar embedding $\mathcal E$ of $G$ such that the number of cycles in $\mathcal C$ that bound a face in $\mathcal E$ is maximized. We establish a tight border of tractability for this problem in biconnected planar graphs by giving conditions under which the problem is NP-hard and showing that relaxing any of these conditions makes the problem polynomial-time solvable. Moreover, we give a $2$-approximation algorithm for series-parallel graphs and a $(4+\varepsilon)$-approximation for biconnected planar graphs.
Stochastic Geometry Methods for Modelling Automotive Radar Interference
Venue: arXiv cs.IT 2016
Year: 2016

As the use of automotive radar increases, performance limitations associated with radar-to-radar interference will become more significant. In this paper we employ tools from stochastic geometry to characterize the statistics of radar interference. Specifically, using two different models for vehicle spacial distributions, namely, a Poisson point process and a Bernoulli lattice process, we calculate for each case the interference statistics and obtain analytical expressions for the probability of successful range estimation. Our study shows that the regularity of the geometrical model appears to have limited effect on the interference statistics, and so it is possible to obtain tractable tight bounds for worst case performance. A technique is proposed for designing the duty cycle for random spectrum access which optimizes the total performance. This analytical framework is verified using Monte-Carlo simulations.
A Novel Model for Distributed Big Data Service Composition using Stratified Functional Graph Matching
Venue: arXiv cs.DB 2016
Year: 2016

A significant number of current industrial applications rely on web services. A cornerstone task in these applications is discovering a suitable service that meets the threshold of some user needs. Then, those services can be composed to perform specific functionalities. We argue that the prevailing approach to compose services based on the "all or nothing" paradigm is limiting and leads to exceedingly high rejection of potentially suitable services. Furthermore, contemporary models do not allow "mix and match" composition from atomic services of different composite services when binary matching is not possible or desired. In this paper, we propose a new model for service composition based on "stratified graph summarization" and "service stitching". We discuss the limitations of existing approaches with a motivating example, present our approach to overcome these limitations, and outline a possible architecture for service composition from atomic services. Our thesis is that, with the advent of Big Data, our approach will reduce latency in service discovery, and will improve efficiency and accuracy of matchmaking and composition of services.
Dealing with Class Imbalance using Thresholding
Venue: arXiv cs.LG 2016
Year: 2016

We propose thresholding as an approach to deal with class imbalance. We define the concept of thresholding as a process of determining a decision boundary in the presence of a tunable parameter. The threshold is the maximum value of this tunable parameter where the conditions of a certain decision are satisfied. We show that thresholding is applicable not only for linear classifiers but also for non-linear classifiers. We show that this is the implicit assumption for many approaches to deal with class imbalance in linear classifiers. We then extend this paradigm beyond linear classification and show how non-linear classification can be dealt with under this umbrella framework of thresholding. The proposed method can be used for outlier detection in many real-life scenarios like in manufacturing. In advanced manufacturing units, where the manufacturing process has matured over time, the number of instances (or parts) of the product that need to be rejected (based on a strict regime of quality tests) becomes relatively rare and are defined as outliers. How to detect these rare parts or outliers beforehand? How to detect combination of conditions leading to these outliers? These are the questions motivating our research. This paper focuses on prediction of outliers and conditions leading to outliers using classification. We address the problem of outlier detection using classification. The classes are good parts (those passing the quality tests) and bad parts (those failing the quality tests and can be considered as outliers). The rarity of outliers transforms this problem into a class-imbalanced classification problem.
Hybrid Recommender System Based on Personal Behavior Mining
Venue: arXiv cs.IR 2016
Year: 2016

Recommender systems are mostly well known for their applications in e-commerce sites and are mostly static models. Classical personalized recommender algorithm includes item-based collaborative filtering method applied in Amazon, matrix factorization based collaborative filtering algorithm from Netflix, etc. In this article, we hope to combine traditional model with behavior pattern extraction method. We use desensitized mobile transaction record provided by T-mall, Alibaba to build a hybrid dynamic recommender system. The sequential pattern mining aims to find frequent sequential pattern in sequence database and is applied in this hybrid model to predict customers' payment behavior thus contributing to the accuracy of the model.
How to Allocate Resources For Features Acquisition?
Venue: arXiv cs.AI 2016
Year: 2016

We study classification problems where features are corrupted by noise and where the magnitude of the noise in each feature is influenced by the resources allocated to its acquisition. This is the case, for example, when multiple sensors share a common resource (power, bandwidth, attention, etc.). We develop a method for computing the optimal resource allocation for a variety of scenarios and derive theoretical bounds concerning the benefit that may arise by non-uniform allocation. We further demonstrate the effectiveness of the developed method in simulations.
Annotation Methodologies for Vision and Language Dataset Creation
Venue: arXiv cs.CV 2016
Year: 2016

Annotated datasets are commonly used in the training and evaluation of tasks involving natural language and vision (image description generation, action recognition and visual question answering). However, many of the existing datasets reflect problems that emerge in the process of data selection and annotation. Here we point out some of the difficulties and problems one confronts when creating and validating annotated vision and language datasets.
Syntactic Phylogenetic Trees
Venue: arXiv cs.CL 2016
Year: 2016

In this paper we identify several serious problems that arise in the use of syntactic data from the SSWL database for the purpose of computational phylogenetic reconstruction. We show that the most naive approach fails to produce reliable linguistic phylogenetic trees. We identify some of the sources of the observed problems and we discuss how they may be, at least partly, corrected by using additional information, such as prior subdivision into language families and subfamilies, and a better use of the information about ancient languages. We also describe how the use of phylogenetic algebraic geometry can help in estimating to what extent the probability distribution at the leaves of the phylogenetic tree obtained from the SSWL data can be considered reliable, by testing it on phylogenetic trees established by other forms of linguistic analysis. In simple examples, we find that, after restricting to smaller language subfamilies and considering only those SSWL parameters that are fully mapped for the whole subfamily, the SSWL data match extremely well reliable phylogenetic trees, according to the evaluation of phylogenetic invariants. This is a promising sign for the use of SSWL data for linguistic phylogenetics.
Incremental Factorization Machines for Persistently Cold-starting Online Item Recommendation
Venue: arXiv cs.LG 2016
Year: 2016

Real-world item recommenders commonly suffer from a persistent cold-start problem which is caused by dynamically changing users and items. In order to overcome the problem, several context-aware recommendation techniques have been recently proposed. In terms of both feasibility and performance, factorization machine (FM) is one of the most promising methods as generalization of the conventional matrix factorization techniques. However, since online algorithms are suitable for dynamic data, the static FMs are still inadequate. Thus, this paper proposes incremental FMs (iFMs), a general online factorization framework, and specially extends iFMs into an online item recommender. The proposed framework can be a promising baseline for further development of the production recommender systems. Evaluation is done empirically both on synthetic and real-world unstable datasets.
A two-sided academic landscape: portrait of highly-cited documents in Google Scholar (1950-2013)
Venue: arXiv cs.DL 2016
Year: 2016

The main objective of this paper is to identify the set of highly-cited documents in Google Scholar and to define their core characteristics (document types, language, free availability, source providers, and number of versions), under the hypothesis that the wide coverage of this search engine may provide a different portrait about this document set respect to that offered by the traditional bibliographic databases. To do this, a query per year was carried out from 1950 to 2013 identifying the top 1,000 documents retrieved from Google Scholar and obtaining a final sample of 64,000 documents, of which 40% provided a free full-text link. The results obtained show that the average highly-cited document is a journal article or a book (62% of the top 1% most cited documents of the sample), written in English (92.5% of all documents) and available online in PDF format (86.0% of all documents). Yet, the existence of errors especially when detecting duplicates and linking cites properly must be pointed out. The fact of managing with highly cited papers, however, minimizes the effects of these limitations. Given the high presence of books, and to a lesser extend of other document types (such as proceedings or reports), the research concludes that Google Scholar data offer an original and different vision of the most influential academic documents (measured from the perspective of their citation count), a set composed not only by strictly scientific material (journal articles) but academic in its broad sense
Deterministic Annealing Optimization for Witsenhausen's and Related Decentralized Stochastic Control Problems
Venue: arXiv cs.SY 2016
Year: 2016

This note studies the global optimization of controller mappings in discrete-time stochastic control problems including Witsenhausen's celebrated 1968 counter-example. We propose a generally applicable non-convex numerical optimization method based on the concept of deterministic annealing-which is derived from information-theoretic principles and was successfully employed in several problems including vector quantization, classification, and regression. We present comparative numerical results for two test problems that show the strict superiority of the proposed method over prior approaches in the literature.
Lagrangian Decomposition based Multi Agent Model Predictive Control for Electric Vehicles Charging integrating Real Time Pricing
Venue: arXiv cs.SY 2016
Year: 2016

This paper presents a real time distributed control strategy for electric vehicles charging covering both drivers and grid players' needs. Computation of the charging load curve is performed by agents working at the level of each single vehicle, with the information exchanged with grid players being restricted to the chosen load curve and energy price feedback from the market, elaborated according to the charging infrastructure congestion. The distributed control mechanism is based on model predictive control methodology and Lagrangian decomposition of the optimization control problem at its basis. The simulation results show the effectiveness of the proposed distributed approach and the mutual coherence between the computed charging load curves and the resulting energy price over the time.
The Vectorization of the Tersoff Multi-Body Potential: An Exercise in Performance Portability
Venue: arXiv cs.CE 2016
Year: 2016

Molecular dynamics simulations, an indispensable research tool in computational chemistry and materials science, consume a significant portion of the supercomputing cycles around the world. We focus on multi-body potentials and aim at achieving performance portability. Compared with well-studied pair potentials, multibody potentials deliver increased simulation accuracy but are too complex for effective compiler optimization. Because of this, achieving cross-platform performance remains an open question. By abstracting from target architecture and computing precision, we develop a vectorization scheme applicable to both CPUs and accelerators. We present results for the Tersoff potential within the molecular dynamics code LAMMPS on several architectures, demonstrating efficiency gains not only for computational kernels, but also for large-scale simulations. On a cluster of Intel Xeon Phi's, our optimized solver is between 3 and 5 times faster than the pure MPI reference.
Computing the atom graph of a graph and the union join graph of a hypergraph
Venue: arXiv cs.DS 2016
Year: 2016

The atom graph of a graph is the graph whose vertices are the atoms obtained by clique minimal separator decomposition of this graph, and whose edges are the edges of all possible atom trees of this graph. We provide two efficient algorithms for computing this atom graph, with a complexity in $O(min(n^α\log n, nm, n(n+\overline{m}))$ time, which is no more than the complexity of computing the atoms in the general case. %\par We extend our results to $α$-acyclic hypergraphs. We introduce the notion of union join graph, which is the union of all possible join trees; we apply our algorithms for atom graphs to efficiently compute union join graphs. Keywords: clique separator decomposition, atom tree, atom graph, clique tree, clique graph, $α$-acyclic hypergraph.